In 2014, Dong et al. [1]} proposed the Super-Resolution Convolutional Neural Network (SRCNN). SRCNN is the first CNN-based SISR model. It shows that a deep CNN model is equivalent to the sparse-coding-based method, which is an example-based method for SISR. Recently, more and more SISR models treat it as an end-to-end learning task. Therefore, building a deep neural network to directly learn the mapping between LR and HR images has become the mainstream method in SISR. Motivated by SRCNN, CNN-based SISR methods are blooming and constantly refreshing the best results.
